Method and image processing system for segmentation of section image data

ABSTRACT

A method is for segmentation of section image data of an examination object, in which a target structure is determined in the section image data and an anatomical norm model is selected whose geometry can be varied on the basis of model parameters. The model parameters are organized hierarchically on the basis of their influence on the anatomical overall geometry of the model. The norm model is matched to the target structure for individualization purposes in a number of iteration steps, with the number of model parameters which can be set being increased in accordance with the hierarchical organization as the number of iteration steps increases. Finally, all of those pixels within the section image data are selected which lie within a contour of the individualized model or of model part, or which differ from this by at most a specific difference value. A corresponding image processing system is also described.

The present application hereby claims priority under 35 U.S.C. §119 onGerman patent application number DE 103 57 206.6 filed Dec. 8, 2003, theentire contents of which are hereby incorporated herein by reference.

FIELD OF THE INVENTION

The invention generally relates to a method for segmentation of sectionimage data of an examination object with the aid of a model based onparameters. The invention also generally relates to a method forproduction of a model based on parameters for use in a segmentationmethod such as this and to an image processing system by which a methodsuch as this can be carried out.

BACKGROUND OF THE INVENTION

The result of investigations by means of modalities which producesection images, such as CT scanners, magnetic resonance appliances andultrasound appliances generally include a number of series with a largenumber of section images of the relevant examination object. For furtherplanning of the examination and/or in order to produce a diagnosis, thissection image data must in many cases be processed further during theexamination itself, or immediately after the examination. The so-called“segmentation” of anatomical structures plays a major role in thefurther processing of this section image data. A segmentation processsuch as this is used to break down the image data of the examinationobject such that specific object elements of an examination object, thatis to say specific anatomical structures which are the focal point ofthe respective examination, are separated from the rest of the imagedata. One obvious example of this is separation of the bone structure ofthe pelvis from a CT or MR section image data record of a patient'slower body.

A further example is contrast agent angiography by way of computedtomography. In an examination such as this it is worthwhile, and maybeabsolutely essential, to remove the interfering bone components from thevolume data record in order subsequently to make it possible to producediagnostically valid MIP representations (MIP=Maximum IntensityProjection) or other result images. This is particularly important forthose examinations in the area of the skull or spinal column. Goodsegmentation of the already existing section image data also plays animportant role in other areas of angiography, for operation planning orelse for selecting the modality for further detailed images of ananatomical structure that is of interest.

One relatively simple segmentation algorithm is the so-called “thresholdvalue method”. This method operates in such a way that the intensityvalues (which are referred to as “Hounsfield values” in computertomography) of the individual voxels, that is to say of the individual3-D pixels, are compared with a fixed threshold value setting. If thevalue of the voxel is above the threshold value, then this voxel isadded to a specific structure. However, this method can be used formagnetic resonance scans, particularly for contrast agent examinationsor in order to separate the external skin surface from the environment.

In the case of computed tomography scans, this method may additionallyalso be used for separation of bone structures. This method is notsuitable for separation of other tissue types. Furthermore,unfortunately, in many cases it is also impossible to use this method toseparate different adjacent bone structures from one another, forexample in order to separate the joint cavity in the pelvis structurefrom the joint head of the femur when scanning a hip joint, and to viewthese object elements separately. Furthermore, a simple threshold valuemethod such as this often cannot be used reliably due to so-calledpartial volume effects and metal artifacts, which ensure that parts ofthe surface of the object element to be separated cannot be determined.

Thus, in many cases, only manual segmentation of the section image datais possible. Unfortunately, however, such manual segmentation is oftenvery difficult to carry out owing to the complicated anatomy of theexamination object, and is associated with a high time penalty.

In principle, the segmentation process can be improved by usingso-called model-based methods, in which morphological knowledge of theexamination object is included in the segmentation process.

Virtual models are already used in many fields of technology in order tosimulate objects in specific states or to recognize objects again. Byway of example, US 2001/0026272 A1 proposes a simulation method inwhich, taking into account mechanical and visual materialcharacteristics of a piece of clothing, the physically correct fit ofthe relevant piece of clothing on the human body can be simulated.Furthermore, for example, U.S. Pat. No. 6,002,782 discloses a method forpersonal identification based on the correlation of recorded 2D imagesof the human face with simulated 2D images of already known optical 3Dface scans, with the x axis of the 3D face scan being made to match theviewing direction of the camera associated with the 2D image.

The medical field, as well, already makes use of methods in order toproduce virtual models of objects to be examined, on the basis of widelydiffering measurements, and these models can then be used as the basisfor the further examination of the relevant object. By way of example,U.S. Pat. No. 6,028,907 describes a method in which a three-dimensionalmodel of a spinal column to be examined is produced from two-dimensionalCT section images and two-dimensional CT scout images.

In the case of the present problems of segmentation of section imagedata, image data which is missing, for example as a result of partialvolume effects or metal artifacts, can be compensated for by matching amodel to a target structure in the section image data (which includesthe object element to be separated) in individual layers. This allowsthe complete reconstruction of the object element to be separated, forexample the organ or the specific bone structure. However, in thismethod, the problem of segmentation is in the end changed to the problemof matching a model as well as possible to a target structure in thesection image data.

SUMMARY OF THE INVENTION

One object of an embodiment of the present invention is to provide acorresponding method and/or an image processing system for simple andreliable segmentation of section image data of an examination objectusing a model. In particular, an object is to provide one wherein themodel can be matched to the target structure satisfactorily, and/or withas little time penalty as possible.

An object can be achieved by a method and/or an image processing system.

On the basis of the method according to an embodiment of the invention,a norm model which is based hierarchically on parameters and in whichthe model parameters are organized hierarchically on the basis of theirinfluence on the anatomical overall structure of the model is in thiscase used as the anatomical norm model, whose geometry can be varied onthe basis of model parameters and can thus be matched to the targetstructure.

The “individualization” of the norm model, that is to say the matchingto the target structure, is in this case carried out in a number ofiteration steps, with the number of model parameters which can be set atthe same time in the respective iteration step, and thus the number ofdegrees of freedom for model variation—being increased corresponding tothe hierarchical order of the parameters as the number of iterationsteps increases. This method ensures that, during the individualizationprocess, those model parameters which have the greatest influence on theanatomical overall geometry of the model are adjusted first of all. Onlythen may the lower-level model parameters, which influence only some ofthe overall geometry, be adjusted, on a gradual basis. This ensures aneffective procedure, which is in consequence time-saving, for modelmatching.

Finally, once the model has been matched in the desired manner to thetarget structure—for example there are no longer any discrepanciesbetween the model and the target structure or the discrepancies areminimal or are below a specific threshold value—all of those pixelswithin the section image data which are within a contour of theindividualized model or of a model element, or which differ from this byat most a specific difference value are selected. The selection processmay in this case be carried out removing the relevant pixels, or byremoving all the other pixels, that is to say with the relevant pixelsbeing cut out. A “model element” should in this case be regarded as apart of the individualized norm model, for example the skull base of askull model.

For this purpose, an image processing system according to an embodimentof the invention requires an interface for reception of the sectionimage data which has been measured by a modality and is to be segmented,a target structure determination unit for determination of the targetstructure in the section image data, a memory device with a number ofcorresponding anatomical norm models for different target structures inthe section image data, with the model parameters being organizedhierarchically on the basis of this influence on the anatomical overallgeometry of the model, and a selection unit for selection of one of theanatomical norm models on the basis of the section image data to besegmented. Furthermore, the image processing system requires anadaptation unit in order to match the selected norm model to the targetstructure in the section image data for individualization purposes in anumber of iteration steps, with the number of model parameters which canbe set being increased in accordance with the hierarchical organizationas the number of iteration steps increases. Finally, the imageprocessing system requires a separation unit in order, finally, toselect all of the pixels within the section image data which lie withina contour of the individualized model or of a model element, or whichdiffer from this by at most a specific difference value.

The model parameters are preferably each associated with one hierarchyclass. This means that different model parameters may possibly also beassociated with the same hierarchy class since they have approximatelythe same influence on the anatomical overall geometry of the model. Allof the model parameters in one specific hierarchy class can then beadded for the first time in one specific situation step in order to beset. The model parameters in the hierarchy class below this are thenadded in the next iteration step, etc.

A model parameter may be associated with a hierarchy class on the basisof a discrepancy in the model geometry which occurs when the relevantmodel parameter is changed by a specific value. In this case, in oneparticularly preferred method, specific areas of discrepancies, forexample numeral discrepancy intervals, are associated with differenthierarchy classes. Thus, for example, a parameter is varied in order toplace this parameter in a hierarchy class, and the resultant discrepancybetween the geometrically changed model and the initial state iscalculated. The extent of the discrepancy in this case depends on thenature of the norm model used.

One feature is that a precisely defined discrepancy measure should bedetermined which quantifies as accurately as possible the geometrychange in the model before and after variation of the relevant modelparameter, in order to ensure realistic comparison of the influence ofthe various model parameters on the model geometry. For this purpose, astandard step width is preferably used for each parameter type, that isto say for example for distance parameters, for which the distancebetween two points in the model is varied, or for angle parameters inwhich an angle between three points in the model is varied, in order toallow the geometry influence to be compared directly. The parameters arethen split between the hierarchy classes simply by presetting numericalintervals for this discrepancy measure.

An uppermost hierarchy class whose model parameters can be setimmediately in a first iteration step preferably contains at least thosemodel parameters whose variation globally changes the norm model. Theseinclude, for example, the total of nine parameters relating to rotationof the overall model about the three model axes, the translation alongthe three model axes, and the scaling of the entire model on the threemodel axes.

The digital anatomical norm models which can be used may in principle beconstructed in widely differing ways. One option, for example, is tomodel anatomical structures on a voxel basis, with specific softwarebeing required for editing of such volume data, although this softwareis generally expensive and is not widely used. Another option is tomodel so-called “finite elements”, with a model generally being formedfrom tetrahedrons. However, specific and expensive software is alsorequired for models such as these.

Simple modeling of anatomical boundary surfaces by triangulation isrelatively widely used. The corresponding data structures are supportedby a large number of standard programs from the computer graphics field.Models constructed on this principle are referred to as so-calledsurface-oriented anatomical models. This is the lowest commondenominator for the modeling of anatomical structures since appropriatesurface models can be derived not only from the first-mentioned volumemodels by triangulation of the voxels but also by changing thetetrahedrons in the finite element method into triangles.

It is thus possible to use surface-oriented models built on a trianglebasis as norm models. First of all, this method allows the models to beproduced very easily and at very low cost. Secondly, models which havealready been produced in a different form, in particular the volumemodels which have been mentioned, can be adopted by appropriatetransformation, so that there is then no need to create an appropriatemodel from new.

Section image scans, for example, can be segmented with correspondingeffort using a classic manual method in order to create such surfacemodels from new. Finally, the models can be generated from theinformation obtained in this way about the individual structures, forexample individual organs. For example, in order to obtain human bonemodels, it is also possible to measure a human skeleton with the aid oflaser scanners, or to scan them, to segment them and triangulate them bymeans of a CT scanner.

When using surface models produced on a triangle basis, the discrepancybetween the unchanged norm model and the changed norm model aftervariation of one parameter is preferably calculated on the basis of thesum of the geometric distances between the corresponding triangles inthe models in the various states.

The hierarchical organization of the individual model parameters may inprinciple be carried out during the segmentation of the section imagedata. It is then possible, for example, to first of all check in eachiteration step which further model parameters have the greatestinfluence on the geometry, and then to add these parameters. However,since this is associated with considerable computation complexity, theclassification or organization of the model parameters in thehierarchical order is particularly preferably done in advance, forexample even while producing the norm model, but at least before thestorage of the norm model in a model database or the like, forsubsequent selection.

Thus, the model parameters are preferably organized in advancehierarchically on the basis of their influence on the anatomical overallgeometry of the model, in an autonomous method for production of normmodels, which are then available for use in said segmentation method.During this process, the model parameters can likewise be associatedwith corresponding hierarchy classes, with a parameter once again beingassociated with a hierarchy class on the basis of the discrepancy in themodel geometry which occurs when the relevant model parameter is changedby a specific value.

This separation of the hierarchical arrangement of the model parametersinto a separate method for production of a norm model has the advantagethat the calculation of the hierarchical organization of modelparameters need be carried out only once for each norm model, thusmaking it possible to save valuable computation time during thesegmentation process. The hierarchical organization can be storedtogether with the norm model in a relatively simple manner, for exampleby organizing the parameters in hierarchy classes or by logicallylinking them with appropriate markers or the like in a file header, orby storing them at another normalized position in the file which alsocontains the further data for the relevant norm model.

There are various options for determination of the target geometry ofthe object element to be separated in the section image data. In onepreferred method, the target geometry is at least partially determinedautomatically by way of a contour analysis method. Contour analysismethods such as these operate on the basis of gradients between adjacentpixels. Widely differing contour analysis methods are known to thoseskilled in the art.

The advantage of contour analysis methods such as these is that themethods can be used not only for CT scanner section image data but alsofor magnetic resonance section image data and for ultrasound sectionimage data. One relatively good alternative is to use the thresholdvalue method, which has already been described in the introduction andin which, for example, the intensity values of the individual pixels areanalyzed to determine whether they exceed a specific threshold value. Ashas already been mentioned, this latter method is, however, suitableonly for determination of the target geometries of the skin surface orfor contrast agent examinations in CT and MR scans as well as for CTscans for determination of bone target structures.

In one particularly preferred variant, a current discrepancy valuebetween the geometry of the modified norm model and of the targetstructure is in each case determined on the basis of a specificdiscrepancy function during the process of matching the norm model tothe target structure. One possible calculation option is to add thesquares of the minimum spatial distances between the model triangles andthe target structure. This discrepancy value may be used in various waydepending on whether the method is being carried out manually,semi-automatically or fully automatically.

In the case of a manual method, the individual model parameters may beoffered the user for variation, for example by means of a graphic userinterface, in each iteration step on the basis of their hierarchicalorganization.

In this case, the current discrepancy value is then preferably alsoindicated, so that the user will see immediately on variation of therelevant model parameter whether and to what extent the geometrydiscrepancies are reduced by his actions. In particular, in this case,it is also possible to determine discrepancy values individually foreach model parameter and to indicate these instead of an overalldiscrepancy value, or in addition to it.

One typical example of this is to display the target structure and/orthe norm model to be matched, or at least parts of these objects on agraphics user interface at a terminal, in which case the user may, forexample, use the keyboard or a pointing device such as a mouse or thelike to adapt a specific model parameter—for example the distancebetween two points on the model. A moving bar or some similar opticallyeasily identifiable means is used to indicate to the user the extent towhich the discrepancies are reduced by his actions, in particulardisplaying on the one hand the total discrepancy of the model and, onthe other hand, the discrepancies relating to the adaptation of thespecific current model parameter—for example in the case of the distancebetween two points in the model, its difference from the distancebetween the relevant points in the target structures.

In consequence, this method can also be used to achieve satisfactorydiscrepancy values with manual matching, in a convenient manner and withrelatively little time penalties.

The discrepancy function can preferably also be used in order to carryout the matching process completely automatically or at least partiallyautomatically. In an automatic matching method such as this, the modelparameters are likewise changed iteratively on the basis of theirhierarchical organization, with the discrepancy function being minimizedoverall.

Automatic matching may in this case be carried out completely in thebackground, so that the operator can carry out other work and, inparticular, can process other image data in parallel, or can controlfurther measurements, on a console of the image processing system whichis carrying out the segmentation process. In this case, it is possiblefor the process to be displayed permanently, for example on a screen(part) while the method is being carried out automatically, so that theuser can monitor the progress in the matching process. In this case, acurrent value of the discrepancy function, possibly only for theparameter which is currently being varied as well, is preferably onceagain displayed to a user. In particular, it is also possible toindicate the discrepancy values on the screen permanently, for examplein a task bar or the like, while the rest of the user interface is freefor other work by the user.

In one very particularly preferred exemplary embodiment, the modelparameters are in each case logically linked to a position of at leastone anatomical landmark of the model such that the model has ananatomically sensible geometry for each parameter set. Typical examplesof this are, on the one hand, global parameters such as rotation ortranslation of the overall model, in which the positions of all of themodel parameters are changed appropriately with respect to one another.Other model parameters are, for example, the distance between twoanatomical landmarks or an angle between three anatomical landmarks, forexample in order to determine a knee position.

Such coupling of the model parameters to medically sensibly selectedanatomical landmarks has the advantage that a diagnostic statement canalways be made after the individualization process. Furthermore, thepositions of such anatomical landmarks are described exactly in theanatomical specialist literature. A procedure such as this thereforemakes it easier to carry out the segmentation process, since a medicallytrained user, for example a doctor or an MTA, is familiar with theanatomical landmarks, and these essentially determine the anatomy.

By way of example, the human pelvis can be described in a known mannerby the following variables:

-   -   Distantia cristarum    -   Distantia spinarum    -   Diameter spinarum posterior    -   Diameter transversa of the pelvis width    -   Diameter transversa of the pelvis narrow section    -   Diameter transversa of the pelvis inferior aperture    -   Diameter sagittalis of the pelvis width    -   Diameter sagittalis of the pelvis narrow section    -   Diameter sagittalis of the pelvis inferior aperture    -   Conjugata anatomica    -   Conjugata diagonalis    -   Conjugata vera        with these variables in turn being derived from a generally        known set of anatomical landmarks.

The selection unit, the adaptation unit and the separation unit for theimage processing system may particularly preferably be in the form ofsoftware in an appropriately suitable image computer processor. Thisimage computer should have an appropriate interface for reception ofimage data and a suitable memory device for anatomical norm models. Inthis case, this memory device need not necessarily be an integrated partof the image computer, and it is sufficient for the image computer to beable to access this and appropriate external memory device. Animplementation of the method according to the invention in the form ofsoftware has the advantage that existing image processing systems canalso be retrofitted appropriately and in a relatively simple manner, bysuitable updates. The image processing system according to the inventionmay, in particular, also be a drive unit for the modality which recordsthe section image data itself, and has the necessary components forprocessing of the section image data according to the invention.

A separate method according to an embodiment of the invention, which iscarried out before the segmentation process, in order to produce a normmodel in which the model parameters are organized hierarchically on thebasis of their influence on the anatomical overall geometry of themodel, may likewise also be in the form of suitable software on acomputer. In particular, in this case, it is also possible for the imageprocessing system which carries out the segmentation process on theimage data to be used for production of the norm models. By way ofexample, free computation capacities could be used at specific times atwhich the image processing system is loaded only lightly by currenttasks in order to produce norm models with hierarchically organizedmodel parameters, and to store them in a database for subsequent use.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be explained in more detail in the following textusing exemplary embodiments and with reference to the attached drawings,in which:

FIG. 1 shows a systematic illustration of one exemplary embodiment of animage processing system according to the invention, which is connectedvia a data bus to a modality and to an image data memory.

FIG. 2 shows a flowchart in order to illustrate one possible procedurefor the segmentation method according to an embodiment of the invention,

FIG. 3 a shows a three-dimensional surface model of a human pelvis,

FIG. 3 b shows the surface model illustrated in FIG. 3 a, with anextended distantia spinarum,

FIG. 4 a shows an illustration of the human pelvis as shown in FIG. 3 a,as a surface model on a triangle basis,

FIG. 4 b shows an illustration of the right-hand section of the pelvisbone as shown in FIG. 4 a,

FIG. 5 a shows an illustration of the target structures of a human skullin the section image data of a CT scanner,

FIG. 5 b shows the target structures as shown in FIG. 5 a with a surfacenorm model which is not yet been adapted,

FIG. 5 c shows an illustration of the target structures and of the normmodel as shown in FIG. 5 b, but with the norm model having beenpartially matched to the target structure,

FIG. 5 d shows an illustration of the target structures and of the normmodel as shown in FIG. 5 c, but with the norm model having been furthermatched to the target structure,

FIG. 6 shows an illustration of possible anatomical landmarks on a humanskull.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

The exemplary embodiment of an image processing system 1 according tothe invention as illustrated in FIG. 1 essentially comprises an imagecomputer 10 and a console 5 or the like connected to it, with a screen6, a keyboard 7 and pointing device, in this case a mouse 8. The imagecomputer 10 may be a computer constructed in the normal manner, forexample a workstation or the like, which can also be used for otherimage evaluation processes and/or for controlling image scanners(modalities) such as CT scanners, magnetic resonance image scanners,ultrasound appliances, etc. The major components within this imagecomputer 10 are, inter alia, a central processor 11 and an interface 13in order to receive section image data D for a patient P which has beenmeasured by a modality 2, in this case a magnetic resonance imagescanner.

In the exemplary embodiment illustrated in FIG. 1, the modality 2 isconnected to a control device 3 which is in turn connected to a bus 4,to which the image processing system 1 is also connected. Furthermore, abulk memory 9 for temporary storage or for permanent storage of theimages recorded by the modality 2 and/or for the image data processedfurther by the image processing system 1 is connected to this bus 4.Other components included in conventional radiological informationsystems (RIS), for example further modalities, bulk memories,workstations, output devices such as printers, filming stations, or thelike, may, of course, also be connected to the bus 4 in order to form alarger network. A connection to an external network or to further RISsis likewise possible. All of the data is in this case preferablyformatted for communication between the individual components using theso-called DICOM Standard (DICOM=Digital Imaging and Communication inMedicine).

The modality 2 is normally driven via the control device 3, which alsoacquires the data from the modality 2. The control device 3 may have itsown console or the like for operation in situ, although this is notillustrated here. However, it is also possible for it to be controlledvia the bus by way of a separate workstation which is located in thevicinity of the modality.

FIG. 2 illustrates one typical procedure for a segmentation methodaccording to an embodiment of the invention.

First of all, the section image data to be evaluated is defined in afirst method step I. The section image data D may, of example, besupplied directly from the modality 2 or its control device 3 via thebus 4 to the image computer 10. However, this may also be section imagedata D which has already been recorded at an earlier time, and has beenstored in a bulk memory 9.

A norm model M for the section image data D to be segmented is thenselected for segmentation in a step II. Thus, an anatomical norm modelis selected in accordance with a questionnaire on which the segmentationprocess is based. For example, a skull model is selected forsegmentation of section image scans of a skull, a pelvis model isselected for segmentation of section image scans of a pelvis bonestructure, and a knee model is selected for segmentation of sectionimage scans of a knee. For this purpose, image computer 10 has a memory12 with widely differing norm models for different possible anatomicalstructures. These may be models which comprise a number of objectelements.

One example of a norm model M which comprises a number of individualobject elements is the pelvis norm model that is illustrated in FIGS. 3a and 3 b, and whose distantia spinarum has already been changed. Asseparable object elements, this includes the left-hand pelvis bone T₁(Os coxae sinister), the right-hand pelvis bone (T₂ (Os coxae dexter),the sacrum T₃ (Os sacrum), the symphysis (T₄ (Symphysis pubica) and thecoccyges T₅ (Os coccyges). In order to assist operator clarity, themodel M is displayed with a smooth surface on a user interface, as inFIGS. 3 a and 3 b. The model on which this illustration is based andwhich is used for segmentation is a surface model on a triangle basis.FIG. 4 a shows a corresponding illustration of the pelvis model M asshown in FIG. 3 b. FIG. 4 b shows the right-hand pelvis bone T₂ from theside, looking at the joint cavity.

In a further step III, which may also be carried out in parallel with orbefore the method step II for model selection, target structures Z aredefined within the section image data D. This may be done fullyautomatically, semi-automatically or completely manually, for examplewith the aid of contour analysis, as has already been mentioned. Athreshold value method may also be used for certain structures andcertain scanning methods, as has already been described further above.

The model M is in this case selected automatically by way of a selectionunit 14 on the basis of the segmentation task, which, by way of example,can be predetermined via the console 5 manually at the start of thesegmentation method, and the determination of a target structure Z bymeans of a target structure determination unit 17, which is in this casein the form of software in the processor 11 in the image computer 10.

As an example, FIG. 5 a shows a skull target structure Z determined froma CT scan of a head, in order to match a skull norm model M to it. Thismatching of the norm model M to the target structure Z is carried outwithin an adaptation unit 15, which is likewise in the form of asoftware module in the processor 11 in the image computer 10.

The individual model parameters are varied for this purpose in anadaptation process in a number of iteration steps S until, in the end,all of the parameters have been individualized, that is to say they havebeen set such that they match, or the individualization is sufficient,that is to say the discrepancy the norm model M and the target structureZ is a minimum or is below a predetermined threshold value. Eachiteration step S in this case comprises a number of process steps IV, V,VI, VII, which are carried out in the form of a loop.

The loop or the first process step S starts with the method step IV, inwhich the optimum parameters for translation, rotation and scaling aredetermined first of all. These are the parameters in the uppermost(referred to in the following text as the “0-th”) hierarchy class, sincethese parameters affect the overall geometry. The three translationparameters t_(x), t_(y), t_(z) and the three rotation parameters r_(x),r_(y), r_(z) are shown schematically around the three spatial axes inFIG. 3.

Once this matching process has been carried out as far as possible,model parameters which have not yet been set are estimated from alreadydetermined parameters in a further step V. Thus, initial values forlower-level parameters are estimated from the settings of higher-levelparameters. One example of this is the estimation of the knee width fromthe settings for a scaling parameter for the body height. This value ispredetermined as the initial value for the subsequent setting of therelevant parameter. This makes it possible to speed up the methodconsiderably. The relevant parameters are then set optimally in themethod step VI.

According to an embodiment of the invention, the parameters areorganized hierarchically on the basis of their influence on theanatomical overall geometry of the model. The greater the geometriceffect of a parameter, the higher it is in the hierarchy. As the numberof iteration steps S increases, the number of model parameters which canbe set is increased at the same time, corresponding to the hierarchicalorganization.

Thus, in the first iteration step S or within the first run through theloop, only the parameters for the 1-th hierarchy level below the 0-thhierarchy level are used for setting the model in the step VI. In thesecond run, it is then possible to once again first of all subject themodel to translation, rotation and scaling in the method step IV. Thosemodel parameters in the 2nd hierarchy class which have not yet beendetermined are then estimated from already determined parameters in themethod step V, and are then added for setting in step VI. This method isthen repeated n-times, with all of the parameters in the n-th stagebeing optimized in the n-th iteration step, and the final step VII inthe iteration step S once again being used to determine whether anyfurther parameters are still available which have not yet beenoptimized.

A new (n+1)-th iteration step is then started once again, with the modelfirst of all being moved, rotated or scaled appropriately once again,and, finally, it is possible to set all of the parameters again inseries, with the parameters in the (n+1)-th class now also beingavailable. Another check is then carried out in the method step VII todetermine whether all of the parameters have been individualized, thatis to say whether there are still any parameters which have not yet beenoptimized, or whether the desired matching has already been achieved.

FIGS. 5 b to 5 d show a very simple case of a matching process such asthis. This figure once again shows the model M as a continuous surface,in order to assist clarity. FIG. 5 b shows the target structure Z withthe model M shifted with respect to it. The image illustrated in FIG. 5c is then obtained by simple translation, rotation and scaling, with themodel M actually being relatively well matched to the target structure Zin this image. Finally, the matching achieved in FIG. 5 d is obtained bysetting further, lower-level parameters.

The iteration method described above ensures that matching is carriedout as effectively and in as time-saving a manner as possible. In thiscase, during the matching process, both the target structure Z and theassociated model M as well as the currently calculated discrepancyvalues and the currently calculated value of a discrepancy function canbe displayed on the screen 6 of the console 5 at any time during thematching process. Further, the discrepancies can also be visualized, asillustrated in FIGS. 5 c to 5 d. In addition, the discrepancy can alsobe visualized by appropriate coloring.

The lower-level hierarchy classes are obtained from quantitativeanalysis of the geometry influence. To do this, each parameter ischanged and the resultant discrepancy between the geometrically changedmodel and the initial state is calculated. This discrepancy may bequantified, for example, by the sum of the geometric distances betweencorresponding model triangles, when triangle-based surface models areused, as illustrated in FIGS. 4 a and 4 b. The parameters can then besplit into the hierarchy class by presetting numerical intervals for thediscrepancy. In this case, it is quite probable that differentparameters will form in the same hierarchy class. This is dependentinter alia on the width of the numerical intervals for thediscrepancies. As explained above, these parameters in the samehierarchy class are offered for changing for the first time at the sametime within a specific iteration step S, or are changed automatically inan appropriate form in an automatic matching method.

As already mentioned, this method preferably makes use of modelparameters which are directly linked to one or more positions ofspecific anatomical markers in the model. On the one hand, this has theadvantage that only medically sensible transformations of the model arecarried out. On the other hand, this has the advantage that themedically trained user will generally know these anatomical landmarksand can thus work quite well with these parameters. One example of aparameter such as this is the distance d₁₂ shown in FIG. 3 a between theanatomical landmarks L₁, L₂, which each occur on the spinae iliacaeanteriores superiores of the pelvis norm model in FIG. 3 a.

In the present implementation of the method according to an embodimentof the invention, the user can, for example, use a mouse pointer toselect one of the anatomical landmarks L₁, L₂ on the spinae iliacaeanteriores superiores and change its position interactively. In thisway, he can vary the distance between the distantia spinarum, that is tosay the length of the distance d₁₂, and can thus vary the entire pelvisgeometry. FIG. 3 a shows a model state in which the distance d₁₂ hasbeen reduced, while FIG. 3 b shows a model state in which the distanced₁₂ has been increased.

In the same way, the user can also, for example, change the distance d₃₄between the anatomical marker L₃ on the top of the sacrum T₃ and theanatomical marker L₄ on the symphysis pubica T₄. Since the twoparameters d₁₂, d₃₄ have approximately the same influence on the overallgeometry of the norm module M of the pelvis, these parameters are inthis case arranged in the same hierarchy class and can be changed by theuser within the same iteration step S, or are varied automatically inthis iteration step S.

Another typical example in which the distances between two landmarks areorganized in different classes, can be explained with reference to theanatomical markers on a skull model. A front view of a skull model withdifferent anatomical landmarks is shown in FIG. 6. A skull model such asthis is defined by parameters including the distance between the twoorbital sockets (eye sockets), that is to say between the markers L₅, L₆positioned there, as well as the difference between the two processistyloidei, which are small bony projections on the base of the skull(although these cannot be seen in the view in FIG. 6).

In this case, the geometrical effect of the first parameter, whichindicates the distance between the orbitals, is greater than thegeometric effect of the second parameter, which indicates the distancebetween the processi styloidei. This can be examined by varying thegeometry of the model while changing the parameters by one millimeter.Since the processi styloidei are relatively small structures, thegeometrical model change is restricted to a small area around these bonyprojections.

In contrast, the orbital sockets are relatively much larger. If thedistance between the orbitals is changed, a component of the model whichoccurs more than once will change its geometry, leading to an increaseddiscrepancy. The parameter represented by the distance between theorbitals is thus arranged in a considerably higher hierarchy class thanthe change in the distance between the processi styloidei since, inprinciple, parameters with a greater geometric range in the parameterhierarchy are at a higher level than parameters with a more localeffect.

When a model parameter d₁₂, d₃₄ which in this case includes, asdescribed here, a distance between two anatomical landmarks L₁, L₂, L₃,L₄ in the norm model M is varied, the geometry of the norm model M ispreferably deformed in an area along a straight line between theanatomical landmarks L₁, L₂, L₃, L₄ in proportion to the change in thedistance. This is illustrated for the distance d₁₂ in the FIGS. 3 a and3 b.

In the event of variation of a model parameter d₁₂, which includes thechange in the position of the first anatomical landmark, in this case asan example the landmark L₁ of the norm model M relative to an adjacentlandmark, for example in this case the landmarks L₃, L₄, the geometry ofthe norm model M is preferably deformed in an appropriate manner in anarea U surrounding the relevant first anatomical landmark L₁ in thedirection of the relevant adjacent landmarks L₃, L₄. In this case, thedeformation advantageously decreases as the distance a from the relevantfirst anatomical landmark L₁ increases. Thus, the deformation in theclose area around the landmark L₁ is greater than in those areas whichare further away from it, in order to achieve the effect illustrated inFIGS. 3 a and 3 b. However, other transformation rules are alsofeasible, provided that they lead to anatomically sensibletransformations. This may be dependent on the respectively selectedmodel.

When, finally, sufficient matching has been achieved, then the actualsegmentation process is carried out in the method step VIII. This isdone in a separation unit 16, which is likewise in the form of asoftware module within the processor 11. In this case, all the pixelswithin the section image data located within a contour of the model orof a part of interest thereof are selected. For this purpose, by way ofexample, the relevant pixels are deleted from the image data or all theother data items are deleted, so that only the desired pixels remain.The separated element can then be processed further as required.

As this point, it should once again be stated expressly that the systemarchitectures and processes illustrated in the figures are onlyexemplary embodiments, whose details may be varied by those skilled inthe art without any problems. In particular, the control device 3(provided that, for example, it has an appropriate console) may alsohave all the corresponding components of the image computer 10 in orderto carry out the image processing in accordance with the methodaccording to the invention directly there.

In this case, in consequence, the control device 3 itself forms theimage processing system according to an embodiment of the invention, andthere is no need for a further workstation or a separate image computer.Incidentally, it is not absolutely essential for the various componentsof an image processing system according to the invention to beimplemented in a processor or in image computer or the like, and, ratherthan this, the various components may also be distributed between anumber of processors or between computers which are networked with oneanother.

Incidentally, it is possible to retrofit existing image processingsystems (in which already known post-processing processes areimplemented) with a process control unit according to the invention inorder additionally to use these systems in accordance with the methodaccording to the invention as described above. In many cases, it maypossibly also be sufficient to update the control software with suitablecontrol software modules.

Exemplary embodiments being thus described, it will be obvious that thesame may be varied in many ways. Such variations are not to be regardedas a departure from the spirit and scope of the present invention, andall such modifications as would be obvious to one skilled in the art areintended to be included within the scope of the following claims.

1. A method for segmentation of section image data of an examinationobject for separation of at least one object element, comprising:selecting an anatomical norm model on the basis of the section imagedata to be segmented, the geometry of the norm model being variable onthe basis of model parameters, the model parameters being organizedhierarchically on the basis of their influence on the anatomical overallgeometry of the model; matching, by an image computer, the norm model toa target structure, determined in the section image data, forindividualization purposes in a number of iteration steps, wherein thenumber of model parameters being increased in accordance with theirhierarchical organization as the number of iteration steps increases;and selecting, by the image computer, all of those pixels within thesection image data, which at least one of lie within a contour of theindividualized model or model element, and which differ from thisindividualized model or model element by at most a specific differencevalue.
 2. The method as claimed in claim 1, wherein the model parametersare each associated with one hierarchy class.
 3. The method as claimedin claim 2, wherein a model parameter is associated with a hierarchyclass on the basis of a discrepancy in the model geometry which occurswhen the relevant model parameter is changed by a specific value.
 4. Themethod as claimed in claim 3, wherein different hierarchy classes areassociated with specific value ranges of discrepancies.
 5. The method asclaimed in claim 4, wherein those model parameters whose variationresults in the norm model are varied globally, are arranged in theuppermost hierarchy class, whose model parameters is setable in a firstiteration step.
 6. The method as claimed in claim 3, wherein those modelparameters whose variation results in the norm model are variedglobally, are arranged in the uppermost hierarchy class, whose modelparameters is setable in a first iteration step.
 7. The method asclaimed in claim 2, wherein those model parameters whose variationresults in the norm model are varied globally, are arranged in theuppermost hierarchy class, whose model parameters is setable in a firstiteration step.
 8. The method as claimed in claim 1, wherein surfacemodels produced on a triangle basis are used as norm models.
 9. Themethod as claimed in claim 8, wherein the discrepancy is determined onthe basis of the sum of the geometric distance between correspondingtriangles of the unchanged norm model and of the changed norm model. 10.The method as claimed in claim 1, wherein the target structure of theobject element to be separated m the section image data is at leastpartially determined automatically by way of a contour analysis method.11. The method as claimed in claim 1, wherein a current discrepancyvalue between the geometry of the modified norm model and the targetstructure is in each case determined on the basis of a specificdiscrepancy function during the matching of the norm model to the targetstructure.
 12. The method as claimed in claim 1, wherein the modelparameters are offered to a user for variation in order to carry outiteration steps, based on their hierarchical organization.
 13. Themethod as claimed in claim 1, wherein the model parameters are changedon the basis of their hierarchical organization in an automatic matchingmethod so as to minimize the discrepancy function.
 14. The method asclaimed in claim 1, wherein a current value of the discrepancy functionis indicated to a user in each iteration step.
 15. The method as claimedin claim 1, wherein the model parameters are each logically linked to aposition of at least one anatomical landmark in the relevant norm modelsuch that the model has an anatomically sensible geometry for eachparameter set.
 16. The method as claimed in claim 15, wherein, when amodel parameter which includes a distance between two anatomicallandmarks in the norm model is varied, the geometry of the norm model isdeformed in proportion to the distance change in an area along astraight line between the anatomical landmarks.
 17. The method asclaimed in claim 16, wherein, when a model parameter which includes achange in the position of a first anatomical landmark of the norm modelrelative to an adjacent landmark is varied, the geometry of the normmodel is also deformed in a matching form in an area surrounding therelevant first anatomical landmark.
 18. The method as claimed in claim15, wherein, when a model parameter which includes a change in theposition of a first anatomical landmark of the norm model relative to anadjacent landmark is varied, the geometry of the norm model is alsodeformed in a matching form in an area surrounding the relevant firstanatomical landmark.
 19. The method as claimed in claim 18, wherein theamount of deformation decreases as the distance from the relevant firstanatomical landmark increases.
 20. A computer program product embodiedin a computer readable medium and having program code means in order tocarry out all the steps of a method as claimed in claim 1 when theprogram product is run on an image processing system.
 21. A method forproduction of a norm model whose geometry is variable on the basis ofmodel parameters, for use in a method as claimed in claim 1, with themodel parameters being organized hierarchically on the basis of theirinfluence on the anatomical overall geometry of the model.
 22. Themethod as claimed in claim 21, wherein the model parameters areassociated with different hierarchy classes.
 23. The method as claimedin claim 22, wherein a model parameter is associated with a hierarchyclass on the basis of a discrepancy in the model geometry which occurswhen the relevant model parameter is changed by a specific value.
 24. Acomputer program product embodied in a computer readable medium andhaving program code means in order to carry out all of the steps in amethod as claimed in claim 23 when the program product is run on acomputer.
 25. A computer program product embodied in a computer readablemedium and having program code means in order to carry out all of thesteps in a method as claimed in claim 22 when the program product is runon a computer.
 26. A computer program product embodied in a computerreadable medium and having program code means in order to carry out allof the steps in a method as claimed in claim 21 when the program productis run on a computer.
 27. An image processing system for segmentation ofsection image data of an examination object for separation of at leastone object element, comprising: an interface for reception of thesection image data which has been measured by a measurement device andis to be segmented; a target structure determination unit fordetermination of a target structure in the section image data; a memorydevice having a number of anatomical norm models for different targetstructures in the section image data, whose geometry is variable on thebasis of model parameters, the model parameters being organizedhierarchically on the basis of their influence on the anatomical overallgeometry of the model; a selection unit for selection of one of theanatomical normal models on the basis of the section image data to besegmented; an adaptation unit in order to match the selected norm modelto the target structure in the section image data for individualizationpurposes in a number of iteration steps, with the number of modelparameters which can be set being increased in accordance with theirhierarchical organization as the number of iteration steps increases;and a separation unit in order to select all of the pixels within thesection image data which at least one of lie within a contour of theindividualized model or model element, and which differ from thisindividualized model or model element by at most a specific differencevalue.
 28. A device for measurement of section image data of anexamination object comprising an image processing system as claimed inclaim
 27. 29. An image processing system for segmentation of sectionimage data of an examination object for separation of at least oneobject element, comprising: means for selecting an anatomical norm modelon the basis of the section image data to be segmented, the geometry ofthe norm model being variable on the basis of model parameters, themodel parameters being organized hierarchically on the basis of theirinfluence on the anatomical overall geometry of the model; means formatching the norm model to a target structure, determined in the sectionimage data, for individualization purposes in a number of iterationsteps, wherein the number of model parameters being increased inaccordance with their hierarchical organization as the number ofiteration steps increases; and means for selecting all of those pixelswithin the section image data, which at least one of lie within acontour of the individualized model or model element, and which differfrom this individualized model or model element by at most a specificdifference value.
 30. A device for measurement of section image data ofan examination object comprising an image processing system as claimedin claim 29.